The in-cylinder convective heat transfer from the combustion gas to the cylinder wall of the combustion chamber has a strong effect on the diesel engine performances. This study contribute to the knowing in this area for the event of the convective heat transfer from fluid to the cylinder wall of a turbocharged diesel engine with six-cylinder direct injection operating under steady-state conditions. An optimized choice of correlation was applied for the evaluation of heat transfer coefficient, which is the Hohenberg\'s correlation. We developed a computer program for simulating the operation of a six-cylinder direct injection turbocharged diesel engine, and using a commercial GT-Power software to validate the simulation results. The influence of certain engine parameters (thermodynamic and geometric) on the convective heat flux and its maximum value are studied in the present paper. The results show that the convective heat transfer characteristics as a function of crank angle of the six cylinder turbocharged diesel engine strongly depend on the engine injection timing, the cylinder wall temperature and load for the both softwares; FORTRAN and GT-Power.

Multi-objective optimization has been applied in many fields of science, including engineering, economics and logistics where optimal decisions need to be taken in the presence of trade-offs between two or more conflicting objectives. One approach to optimize a multi-objective mathematical model is to employ utility functions for the objectives. Recent studies on utility based multi-objective optimization concentrate on considering just one utility function for each objective. But, in reality it is not reasonable to have a unique utility function corresponding to each objective function. Here, a constrained multi-objective mathematical model is considered in which several utility functions are associated for each objective. All of these utility functions are uncertain and in fuzzy form, so a fuzzy probabilistic approach is incorporated to investigate the uncertainty of the utility functions for each objective. Meanwhile, the total utility function of the problem will be a fuzzy nonlinear mathematical model. Since there are not any conventional approaches to solve such a model, a defuzzification method to change the total utility function to a crisp nonlinear model is employed. Also, Maximum technique is applied to defuzzify the conditional utility functions. This action results in changing the total utility function to a crisp single objective nonlinear model and will simplify the optimization process of the total utility function. The effectiveness of the proposed approach is shown by solving a test problem.

Nowadays, due to increase of environmental hazards and legislation in this context by governments and also restriction of manufacturing resources, researchers paid special attention to the design of closed-loop green supply chain network. To establish better coordination between the components of the supply chain and gain more profits in the network, special decisions are requiring during the product lifecycle. The network presented in this study consists of four layers in the forward chain including suppliers, manufacturers, distribution centers and customer markets and also includes three facilities containing collection, dismantler and disposal centers in reverse chain. A mixed integer linear programming model proposed to optimize closed-loop green supply chain by considering the level of quality for constituent components of manufacturing parts along with the pricing policy and product life cycles to maximize profits. Genetic algorithm and particle swarm optimization are used to find the optimal solutions. Analyzing the results and due to the relative percentage deviation and solution time, it was found that genetic algorithm performs better compared with the particle swarm optimization.

Since product pricing is a significant decision for producers and is known as a challenging problem in today’s marketing operations, the aim of this study is to design an integrated decision support system for pricing. The emerging business environment is highly dynamic in which only companies being higher in terms of competitiveness can succeed in achieving a sustainable market. Nowadays, most companies often use complicated information systems such as business intelligence systems for effective decision making and analytics. Here, by using pricing methods the prices of products are determined on the basis of marketing data under the terms of risk so that to maximize revenue along with fulfilling customers’ demands. A case study is reported to show the effectiveness of the approach. There, we analyzed different effects of our proposed pricing models and studied the influences on the purchase behavior of the customers.

With the growing data available on the Internet, customization of the web sites information has become a requirement for users. A procedure for the appropriate customization of web data is configured by automatic extraction of combined knowledge of the log file and user profile information. In this paper, integrating decision tree and association rules for user profile information and log information of website in an online shopping store is targeted. The tangible results of such a framework for decision makers and marketers are customization of web pages and statistical analysis for sale improvement. Applying association rules the website users’ patterns are mined and utilizing decision tree users are classified and their interests are determined. By combining the results of two algorithms and its analysis, the behavior models from user profile, user interests in terms of age and gender, and the most visited web pages by subject can be achieved.

This study investigates the Trump election, an unpredicted outcome along with large policy differentiation between the two political leaders, caused significant reactions on the stock returns of indices of major five countries. Event study is used as a methodology to measure the abnormal returns of indices. Indices return data are collected for sixty days before and after the election event. Results showed negative abnormal returns on the day of election as well as prior to and after the Election Day. This study helps the investor to evaluate the effect of political event on indices returns and formulate the policies in accordance with political situation. Political environment may differ from country to country and further researches can be made by selecting indices of different countries to see the effect of election. This study verifies the considerable connection between political events and stock returns.